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While humans process language incrementally, the best language encoderscurrently used in NLP do not. Both bidirectional LSTMs and Transformers assumethat the sequence that is to be encoded is available in full, to be processedeither forwards and backwards (BiLSTMs) or as a whole (Transformers). Weinvestigate how they behave under incremental interfaces, when partial outputmust be provided based on partial input seen up to a certain time step, whichmay happen in interactive systems. We test five models on various NLU datasetsand compare their performance using three incremental evaluation metrics. Theresults support the possibility of using bidirectional encoders in incrementalmode while retaining most of their non-incremental quality. The"omni-directional" BERT model, which achieves better non-incrementalperformance, is impacted more by the incremental access. This can be alleviatedby adapting the training regime (truncated training), or the testing procedure,by delaying the output until some right context is available or byincorporating hypothetical right contexts generated by a language model likeGPT-2.

Incremental Processing in the Age of Non-Incremental Encoders: An Empirical Assessment of Bidirectional Models for Incremental NLU